Gastos Casa
09 September, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 20/7/2024 | VTR | 21990 | Andrés | NA |
| 12/7/2024 | Uber | 2537 | Tami | Vuelta Uber casa delox |
| 25/7/2024 | Comida | 36175 | Andrés | lider pickup |
| 26/7/2024 | Gas | 72850 | Andrés | NA |
| 27/7/2024 | Comida | 4300 | Andrés | biblioteca san camilo |
| 27/7/2024 | Comida | 27300 | Andrés | NA |
| 28/7/2024 | Comida | 67250 | Tami | Supermercado |
| 30/7/2024 | Comida | 30650 | Andrés | piwén |
| 1/8/2024 | Electricidad | 209425 | Andrés | NA |
| 3/8/2024 | Comida | 45130 | Tami | Supermercado |
| 3/8/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 11/8/2024 | Comida | 8230 | Tami | Supermercado |
| 8/8/2024 | Comida | 5000 | Andrés | platano y cosas |
| 15/8/2024 | Electricidad | 190084 | Andrés | enel rqlo (dupliqué el gasto para que pagues eso. Yo ya ni uso mi estufa, y tal vez tú deberías usar la mía porque tiene temporizador) |
| 18/8/2024 | VTR | 21990 | Andrés | NA |
| 16/8/2024 | Comida | 11756 | Andrés | lider |
| 19/8/2024 | Comida | 91135 | Tami | Supermercado |
| 23/8/2024 | Otros | 170000 | Andrés | instalacion |
| 23/8/2024 | Otros | 285000 | Andrés | aire |
| 25/8/2024 | Gas | 75000 | Andrés | NA |
| 25/8/2024 | Comida | 66781 | Tami | Supermercado |
| 29/8/2024 | Comida | 45000 | Andrés | piwen |
| 2/9/2024 | Comida | 42057 | Tami | Supermercado |
| 4/9/2024 | Agua | 11723 | Andrés | NA |
| 4/9/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 7/9/2024 | Comida | 15960 | Tami | Supermercado |
| 8/9/2024 | Gas/Bencina | 20182 | Tami | Parafina |
| 8/9/2024 | Comida | 55162 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 7.3230e+08 2 5.9125 0.0028 **
## lag_depvar 1.5288e+11 1 2468.6855 <2e-16 ***
## Residuals 4.6756e+10 755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -22.85014 14480.53 0.0509382
## 2-0 30490.206 23942.95151 37037.46 0.0000000
## 2-1 23261.368 19446.85956 27075.88 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 249 26932.43 2 37430.14
## 250 33729.86 2 26932.43
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## 252 44028.00 2 38081.43
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## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
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## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
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## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
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## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
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## 346 41633.57 2 41917.86
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## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
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## 383 46969.57 2 48946.14
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## 399 41704.43 2 56862.71
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## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
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## 443 38988.14 2 41408.14
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## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
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## 455 35547.57 2 47139.43
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## 463 50720.71 2 50490.00
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## 465 52145.57 2 52720.71
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## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
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## 505 45011.43 2 52221.43
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## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
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## 527 53924.00 2 48911.00
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## 531 62329.29 2 47835.71
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## 536 55448.71 2 61137.43
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## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
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## 549 43829.14 2 44061.57
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## 553 50565.00 2 49242.43
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## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
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## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
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## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
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## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
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## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 603 52724.47 17681.050
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2076.04577 4064.86541 -562.35013 2417.02449 -3017.64060 501.29616
## 8 9 10 11 12 13
## -5681.50486 -1158.72999 -3934.02703 -355.31830 -4885.99258 -1518.08576
## 14 15 16 17 18 19
## -809.00012 460.61831 -3179.19178 -294.89015 -2058.95378 6682.50286
## 20 21 22 23 24 25
## -1532.38082 -1200.97324 1488.89969 -1195.06049 233.93434 1686.80114
## 26 27 28 29 30 31
## -7131.44218 988.06723 8213.18784 349.05654 -83.70968 -2466.46517
## 32 33 34 35 36 37
## 1537.62546 4517.86694 1028.17924 2288.77080 -1986.80640 4517.78084
## 38 39 40 41 42 43
## 4250.58913 -2365.48401 -3037.44289 -1129.56308 -10747.69413 7391.32319
## 44 45 46 47 48 49
## 2572.70705 1354.23055 8079.98607 582.34288 6430.74430 6562.77891
## 50 51 52 53 54 55
## -6082.64886 -4911.81226 -5113.66599 -7925.29249 6211.88592 -4066.88513
## 56 57 58 59 60 61
## -4845.31929 3947.34564 928.64598 -5.81686 165.08446 -4978.32728
## 62 63 64 65 66 67
## 18192.34718 3515.47421 -3792.46305 5833.72421 7204.11320 14442.54661
## 68 69 70 71 72 73
## 1374.34801 -13508.62185 -1432.23233 4546.15324 -5032.33858 -4470.99332
## 74 75 76 77 78 79
## -10511.53360 2559.95281 -5343.42792 1167.09886 -6786.94885 685.09577
## 80 81 82 83 84 85
## -2239.62487 -2569.99129 -3796.77792 -380.13141 2457.25576 3863.62386
## 86 87 88 89 90 91
## 526.73952 -446.27819 234.46741 4332.57427 -1180.05948 1147.23501
## 92 93 94 95 96 97
## -2079.66509 -1037.18707 193.89727 286.82358 -7476.71331 2473.30141
## 98 99 100 101 102 103
## -8555.71473 -2813.10717 -3898.55557 -1572.94303 -1100.81969 3333.77614
## 104 105 106 107 108 109
## -2240.95358 2705.67337 -1087.41665 1043.81334 2640.99757 -3133.82353
## 110 111 112 113 114 115
## -4673.60909 -759.69894 1990.90457 11750.30777 -1311.51662 2620.45612
## 116 117 118 119 120 121
## 4193.51358 3398.70359 -1226.49961 -4816.13562 -3763.99236 2322.20825
## 122 123 124 125 126 127
## -1754.27067 1338.70711 8842.87853 743.88331 31.36675 -2609.49672
## 128 129 130 131 132 133
## 2603.09632 6979.95956 877.47815 -8627.90642 1722.37607 4094.03838
## 134 135 136 137 138 139
## -3242.32992 -1456.63882 -872.25492 -3887.71466 1215.14516 -479.73546
## 140 141 142 143 144 145
## -2895.20886 1763.30175 -1859.36761 -7791.70782 2151.01934 -3403.22595
## 146 147 148 149 150 151
## 2203.80014 -190.40075 1083.79193 -316.98929 1392.35136 1207.61660
## 152 153 154 155 156 157
## 3362.48401 -4890.97424 -1151.26912 -3204.07838 6016.73207 9738.47908
## 158 159 160 161 162 163
## -3506.92546 -4833.83528 3579.25419 112.01456 2597.06872 -6049.53294
## 164 165 166 167 168 169
## -6833.82028 4124.93888 17297.08962 3334.34362 -719.38163 -2748.43191
## 170 171 172 173 174 175
## -1369.00704 3344.39761 -507.29070 -8343.27259 2687.93128 4115.31402
## 176 177 178 179 180 181
## 369.84667 8493.79923 -9591.64562 -3700.87795 -10934.95904 -11320.04616
## 182 183 184 185 186 187
## 1256.68729 9272.64438 -1573.04736 5791.65780 6344.91413 12875.55613
## 188 189 190 191 192 193
## 8012.45645 -4550.74508 2050.57400 9948.42390 -2154.79941 -2905.25039
## 194 195 196 197 198 199
## -10687.97489 -6638.45225 1032.44782 -5451.55114 -9958.72257 5319.66656
## 200 201 202 203 204 205
## -3210.20907 -1831.07080 -917.18443 6376.10089 9677.44368 259.09235
## 206 207 208 209 210 211
## 2608.29608 2758.51997 5423.00081 12423.55653 -6217.92887 -11727.39550
## 212 213 214 215 216 217
## -5948.19373 -10799.95958 -5172.38829 1470.15514 -13104.47299 16423.36896
## 218 219 220 221 222 223
## 7629.40611 1254.46927 26402.04471 11947.10275 6656.60454 13319.68713
## 224 225 226 227 228 229
## -4720.15017 -2434.18731 3159.78583 -252.06026 2177.72695 8448.71964
## 230 231 232 233 234 235
## 5217.40307 -2532.85908 -2382.37603 8933.14702 -12071.92277 -7677.83379
## 236 237 238 239 240 241
## -8834.45353 -10292.34102 2993.85239 1216.66314 -8458.67093 -9067.06161
## 242 243 244 245 246 247
## 9098.67924 -7891.08648 2435.33635 -10402.61658 -4056.44667 1437.27921
## 248 249 250 251 252 253
## 971.34508 -12383.65099 3692.74590 2036.04518 4136.24659 1991.76449
## 254 255 256 257 258 259
## -1339.54837 10965.58768 20572.27657 2656.47660 -4813.81694 3650.89809
## 260 261 262 263 264 265
## -2174.59795 3304.72422 -5304.22947 -11265.67773 -4961.05855 -701.72913
## 266 267 268 269 270 271
## -5370.32775 8647.26413 -4527.25544 3990.58735 -2361.17716 4200.51918
## 272 273 274 275 276 277
## 423.92078 7012.68941 -1784.03456 11682.56003 -5059.20775 1328.26585
## 278 279 280 281 282 283
## -774.10699 7470.34470 -5517.29526 -3107.07337 -11590.01565 -2854.06586
## 284 285 286 287 288 289
## 18494.29280 7388.71104 2261.93620 -1108.50494 459.72633 5963.15314
## 290 291 292 293 294 295
## 6391.01713 -19319.17632 -11421.41671 -8262.12345 9612.81052 2880.36223
## 296 297 298 299 300 301
## -1414.09005 27180.58368 9503.95034 4252.51385 8856.95890 2127.80459
## 302 303 304 305 306 307
## -1738.45974 7258.63974 -24983.19516 -3865.10673 -448.57039 -7233.12799
## 308 309 310 311 312 313
## -4144.26409 2804.16023 -9367.53047 -3294.47736 -8227.02397 1608.06726
## 314 315 316 317 318 319
## -3158.39890 2055.94513 -4128.14214 27429.70477 -1133.02868 2909.92129
## 320 321 322 323 324 325
## 10422.83745 5062.92501 31817.05680 4176.69605 -21849.76854 1229.93571
## 326 327 328 329 330 331
## 565.88488 -6985.14024 -2135.89505 -33625.20663 1003.37596 -2224.69117
## 332 333 334 335 336 337
## -13.70671 -3115.67378 4153.00898 -450.98488 -6979.60857 -3070.20571
## 338 339 340 341 342 343
## -2130.32375 -7617.06122 3986.68552 -1323.90504 -1699.30961 -958.24740
## 344 345 346 347 348 349
## 200.00459 478.64093 -1649.21532 -9474.50556 -13133.02290 2529.14970
## 350 351 352 353 354 355
## -4182.68272 -3499.48431 -5813.25236 1954.68483 1518.08472 2829.03352
## 356 357 358 359 360 361
## -3760.60462 -485.43173 687.34780 6989.26587 136.67555 -189.57917
## 362 363 364 365 366 367
## 2426.07964 -2947.13335 -1036.59025 -8893.98235 -4661.01843 -6201.00008
## 368 369 370 371 372 373
## -4875.51589 -7140.73224 5191.05595 434.77380 7147.71395 -7732.28459
## 374 375 376 377 378 379
## -2265.32242 -3380.36623 -2435.18979 -12416.71331 2087.58262 -10519.17668
## 380 381 382 383 384 385
## 5920.66235 9432.74346 3058.74450 -2525.54295 1501.55581 6606.82685
## 386 387 388 389 390 391
## 11171.49896 -6197.86089 -5661.23798 -374.42620 8347.94617 1479.99699
## 392 393 394 395 396 397
## 10873.98384 -10368.92607 2448.32987 359.28777 213.53927 -996.34278
## 398 399 400 401 402 403
## -883.00962 -14788.17245 8438.86355 -1392.93265 -1564.92286 6809.11401
## 404 405 406 407 408 409
## -8205.27504 -1447.83445 -2666.38469 -5921.77548 -2886.28639 -3919.54675
## 410 411 412 413 414 415
## -8721.29451 6267.22344 1650.97836 -7409.09898 -7641.02755 14353.75087
## 416 417 418 419 420 421
## 3706.97178 4314.67571 -8282.39219 -4871.13226 -2666.49594 2778.96378
## 422 423 424 425 426 427
## -14107.66363 -2699.24741 -8997.66584 3210.87084 7087.93061 6551.42426
## 428 429 430 431 432 433
## -4125.43295 -4208.17817 -4762.04995 -1778.70898 -5697.71217 -6554.40053
## 434 435 436 437 438 439
## -5813.34346 -1210.23987 -687.91181 -4843.54789 2746.25495 4926.28008
## 440 441 442 443 444 445
## -5070.82883 -2121.17264 1618.03837 -3844.10556 2862.08421 -6616.11590
## 446 447 448 449 450 451
## -12072.68627 -4329.26193 9848.37332 -2008.08244 4783.59216 -5930.47387
## 452 453 454 455 456 457
## -1114.45790 386.87094 3003.67477 -12350.58154 3446.93226 -6699.42448
## 458 459 460 461 462 463
## 6596.72573 2967.41461 2405.53531 -3990.17574 1998.87393 -139.02111
## 464 465 466 467 468 469
## 1657.04968 -685.90054 3192.47035 -2844.85649 5641.19749 -7186.03869
## 470 471 472 473 474 475
## -3100.83788 -2301.93273 -4735.70005 2980.08259 7723.29676 -6208.77631
## 476 477 478 479 480 481
## 1380.38633 -6308.81387 -2891.41132 1992.00423 -12993.19312 -9652.25748
## 482 483 484 485 486 487
## -997.25598 198.33299 -824.68966 -1226.66252 -9484.00744 11295.38280
## 488 489 490 491 492 493
## 6240.73471 7320.63365 -5645.14750 5239.11508 9087.60227 5726.45370
## 494 495 496 497 498 499
## -13863.98490 -10743.32546 -3470.16152 -1101.65529 -522.98537 -7635.12099
## 500 501 502 503 504 505
## 689.98595 4331.67675 5471.42474 534.69122 -56.84596 -7378.72291
## 506 507 508 509 510 511
## 528.22264 -5110.11418 1829.56878 -1341.35654 -8197.21683 -543.43032
## 512 513 514 515 516 517
## -2632.24503 -531.87312 1371.64307 -9496.33783 -7655.50101 24469.95505
## 518 519 520 521 522 523
## 9638.61147 5562.78787 -5713.14249 2519.89718 16717.20830 10956.96401
## 524 525 526 527 528 529
## -24779.36273 -5304.62363 -3897.99556 4459.94852 -536.20350 -11274.38593
## 530 531 532 533 534 535
## 4373.24254 13815.68324 -5267.66650 4166.12764 5293.26399 -2116.02545
## 536 537 538 539 540 541
## -4822.32244 -7278.33264 -2201.65922 8244.70172 -72.41398 -8337.01292
## 542 543 544 545 546 547
## 1736.89245 -711.57609 258.10137 -11148.82784 -11033.05090 2198.59404
## 548 549 550 551 552 553
## 7096.92821 -1348.48085 809.82077 -7773.74543 8605.41809 807.99759
## 554 555 556 557 558 559
## -12061.59960 9202.86430 8549.75858 -133.38474 4627.78466 -3858.20722
## 560 561 562 563 564 565
## 13887.95407 21092.54680 -7137.75195 -10203.27025 6427.92305 -190.88854
## 566 567 568 569 570 571
## 3065.40751 -7785.74108 -17584.54887 6639.68118 6307.86939 1686.68049
## 572 573 574 575 576 577
## 2865.87491 1506.43404 -2437.85747 14489.37826 -10067.57257 -6500.59855
## 578 579 580 581 582 583
## 8552.88078 2582.86521 -6844.67867 7316.92559 -4088.23260 -2995.17752
## 584 585 586 587 588 589
## 15529.89261 -14882.02944 8269.67020 -200.68688 -6468.88238 -912.07400
## 590 591 592 593 594 595
## 107.00825 -10800.44817 1795.52073 -7185.91489 3111.78405 8847.49048
## 596 597 598 599 600 601
## -7653.84786 5801.14199 2594.67372 6678.40218 -3455.88018 5942.09964
## 602 603 604 605 606 607
## -8580.57188 2102.88305 1089.31034 2946.05822 1267.67501 161.10973
## 608 609 610 611 612 613
## -6039.63805 7935.81596 -1430.24943 -2790.35189 -3623.85424 -8346.62028
## 614 615 616 617 618 619
## 11950.28382 4753.73293 -9556.47902 11523.25124 5788.69301 -5898.40842
## 620 621 622 623 624 625
## 26133.94692 -13387.69659 -7115.18550 2939.22988 -4406.72070 -10768.55590
## 626 627 628 629 630 631
## 11267.86135 -21824.54149 -2310.48704 8784.22326 11102.57368 -1742.69909
## 632 633 634 635 636 637
## 33123.28474 -7180.32022 5268.99201 4916.47180 -2777.21624 -5775.66113
## 638 639 640 641 642 643
## -2261.55989 -12701.71956 -2331.29885 -1949.15018 -2565.03125 -2878.45460
## 644 645 646 647 648 649
## 1820.29530 4397.47616 16863.12812 18227.03934 339.02172 4284.28798
## 650 651 652 653 654 655
## 10091.11220 19540.71443 -63.06835 -28792.50339 -1627.73561 -2535.81714
## 656 657 658 659 660 661
## 1672.99277 -3397.43581 -10771.14307 1659.00215 4189.16447 -1104.43318
## 662 663 664 665 666 667
## 12948.06804 1109.47652 1564.65161 -11943.72790 1293.72381 1083.97427
## 668 669 670 671 672 673
## -5282.38829 -7457.57594 2108.05229 -3711.46967 2709.55759 -3391.33797
## 674 675 676 677 678 679
## -9316.18007 -8184.19663 -2781.85647 367.52611 3002.86463 802.45608
## 680 681 682 683 684 685
## -3768.99355 -1722.90499 -1233.05611 -8163.55863 4806.40901 -2173.13770
## 686 687 688 689 690 691
## -1322.06911 659.41645 10897.75647 9744.65592 10401.47468 -9995.08299
## 692 693 694 695 696 697
## -3735.36879 -3264.78610 5789.73987 -10537.29186 -7928.21127 -8540.45456
## 698 699 700 701 702 703
## -6119.70246 -4540.01277 3301.55107 -4257.91322 -1730.96524 4380.00035
## 704 705 706 707 708 709
## 31184.04821 9235.55095 23090.72768 1122.70464 7817.82682 22391.44668
## 710 711 712 713 714 715
## 5861.53929 -18879.67997 4423.62405 -5842.93366 -395.47380 219.77019
## 716 717 718 719 720 721
## -17502.42776 -5295.60219 3357.85217 -3031.77539 -12967.09392 4420.06026
## 722 723 724 725 726 727
## -5481.97499 860.77847 -3843.13971 -12330.66198 1594.22889 -1687.44810
## 728 729 730 731 732 733
## -9605.49916 17516.26084 1808.09574 -2716.63360 5744.65478 -8668.11272
## 734 735 736 737 738 739
## -670.80991 8187.67750 -15396.77624 -5795.58314 7566.02127 -4727.64087
## 740 741 742 743 744 745
## 255.85780 1904.82918 -1911.65700 -5113.36849 6509.75271 -6260.76247
## 746 747 748 749 750 751
## 22772.00002 7652.60487 -2183.93999 -7478.84037 23321.42478 -4618.37398
## 752 753 754 755 756 757
## 1151.81901 -14653.23729 56052.04367 26298.14083 14279.39374 -11510.34032
## 758 759 760
## 9960.88975 6642.58775 5148.15050
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17193.24 20074.13 24378.49 24093.12 26474.35 23775.42 24500.22 19675.87
## 10 11 12 13 14 15 16 17
## 19409.31 16720.60 17507.28 14197.94 14249.71 14922.24 16638.91 14939.03
## 18 19 20 21 22 23 24 25
## 15985.95 15352.07 22518.38 21591.54 21065.24 22977.63 22295.64 22955.91
## 26 27 28 29 30 31 32 33
## 24823.73 18680.22 20426.81 28356.94 28415.28 28084.32 25685.66 27104.70
## 34 35 36 37 38 39 40 41
## 30993.25 31345.80 32771.66 30252.79 34192.41 37438.48 34459.73 31232.85
## 42 43 44 45 46 47 48 49
## 30066.98 20534.96 28142.72 30608.06 31710.16 38629.23 38117.83 42835.22
## 50 51 52 53 54 55 56 57
## 47121.65 39733.10 34237.24 29201.01 22264.26 28628.74 25168.89 21422.65
## 58 59 60 61 62 63 64 65
## 25883.21 27157.67 27458.20 27874.90 23696.94 40484.67 42350.46 37540.13
## 66 67 68 69 70 71 72 73
## 41796.89 46770.74 57565.22 55555.48 40623.95 38100.28 41153.91 35386.56
## 74 75 76 77 78 79 80 81
## 30784.96 21378.33 24617.71 20495.19 22605.95 17441.05 19480.34 18697.71
## 82 83 84 85 86 87 88 89
## 17713.92 15759.99 17052.89 20703.66 25173.69 26175.28 26200.53 26824.57
## 90 91 92 93 94 95 96 97
## 30998.49 29815.19 30826.38 28867.90 28058.25 28430.75 28842.14 22343.56
## 98 99 100 101 102 103 104 105
## 25394.29 18342.25 17184.84 15202.37 15505.68 16191.08 20716.67 19789.33
## 106 107 108 109 110 111 112 113
## 23341.99 23129.47 24825.43 27736.25 25204.75 21606.13 21884.81 24562.41
## 114 115 116 117 118 119 120 121
## 35555.52 33726.97 35586.20 38620.01 40599.07 38260.14 33019.85 29317.93
## 122 123 124 125 126 127 128 129
## 31425.41 29685.01 30880.55 38570.26 38208.49 37258.93 34085.33 35887.61
## 130 131 132 133 134 135 136 137
## 41349.38 40783.05 31880.62 33160.39 36387.90 32756.07 31124.25 30198.43
## 138 139 140 141 142 143 144 145
## 26714.71 28145.88 27912.78 25571.70 27620.08 26228.56 19754.98 22821.37
## 146 147 148 149 150 151 152 153
## 20622.34 23634.69 24181.07 25790.28 25974.51 27648.24 28964.37 32032.40
## 154 155 156 157 158 159 160 161
## 27448.98 26703.22 24229.55 30193.38 41527.35 39837.84 37171.60 42251.27
## 162 163 164 165 166 167 168 169
## 43676.50 47132.82 42545.11 37796.78 43286.20 59781.23 62019.52 60414.86
## 170 171 172 173 174 175 176 177
## 57203.01 55583.32 58317.86 57330.42 49531.35 52388.26 56175.15 56211.77
## 178 179 180 181 182 183 184 185
## 63424.93 53814.88 50527.39 41227.33 32666.60 36216.36 46439.33 45888.91
## 186 187 188 189 190 191 192 193
## 51912.09 57725.02 68635.54 73980.89 67601.00 67796.72 74950.66 70575.96
## 194 195 196 197 198 199 200 201
## 66045.83 55162.45 49121.98 50563.12 46105.72 38181.90 44682.64 42889.07
## 202 203 204 205 206 207 208 209
## 42522.76 43006.76 49881.13 58875.48 58500.70 60245.91 61921.28 65757.30
## 210 211 212 213 214 215 216 217
## 75335.79 67324.97 55374.34 49919.39 40809.25 37730.99 40881.47 30783.63
## 218 219 220 221 222 223 224 225
## 47957.88 55365.25 56277.81 79312.47 86896.11 88923.03 96604.15 87448.04
## 226 227 228 229 230 231 232 233
## 81375.50 80952.49 77562.84 76714.42 81507.45 82887.86 77257.52 72413.85
## 234 235 236 237 238 239 240 241
## 78134.35 64624.26 56566.60 48422.06 39934.43 44175.91 46354.10 39727.35
## 242 243 244 245 246 247 248 249
## 33332.18 43736.23 37915.09 41897.33 34069.73 32760.29 36458.80 39316.08
## 250 251 252 253 254 255 256 257
## 30037.11 36045.38 39891.75 45147.95 47898.41 47384.98 57807.72 75511.81
## 258 259 260 261 262 263 264 265
## 75324.67 68556.24 70055.60 66231.70 67694.94 61378.82 50526.63 46507.01
## 266 267 268 269 270 271 272 273
## 46718.90 42779.59 51687.83 47916.84 52112.61 50206.91 54322.36 54621.88
## 274 275 276 277 278 279 280 281
## 60710.46 58316.73 68104.06 61957.02 62169.54 60499.08 66309.87 59966.22
## 282 283 284 285 286 287 288 289
## 56489.44 45918.21 44295.99 61732.00 67327.49 67741.79 65128.85 64205.42
## 290 291 292 293 294 295 296 297
## 68253.70 72210.18 52981.99 42966.98 36907.19 47350.64 50630.80 49734.27
## 298 299 300 301 302 303 304 305
## 74216.76 80232.49 80908.04 85575.05 83752.32 78723.79 82231.62 56833.54
## 306 307 308 309 310 311 312 313
## 53050.43 52726.41 46443.12 43619.55 47265.53 39729.62 38436.60 32933.79
## 314 315 316 317 318 319 320 321
## 36763.11 35934.77 39811.57 37772.15 63863.60 61679.22 63322.02 71414.79
## 322 323 324 325 326 327 328 329
## 73830.37 99613.59 97972.05 73516.21 72299.83 70637.71 62494.18 59582.35
## 330 331 332 333 334 335 336 337
## 29175.05 32906.26 33350.99 35698.39 35031.42 40866.70 41955.04 37146.35
## 338 339 340 341 342 343 344 345
## 36351.47 36479.63 31743.17 37813.19 38484.45 38745.96 39632.14 41439.22
## 346 347 348 349 350 351 352 353
## 43282.79 43031.51 35892.59 26348.71 31756.68 30604.20 30189.40 27777.60
## 354 355 356 357 358 359 360 361
## 32511.92 36310.68 40827.18 38994.72 40269.94 42433.73 49916.61 50473.72
## 362 363 364 365 366 367 368 369
## 50677.78 53170.13 50623.73 50061.70 42619.73 39783.29 35914.94 33667.30
## 370 371 372 373 374 375 376 377
## 29678.37 37052.65 39366.71 47345.71 41245.89 40686.51 39206.48 38733.71
## 378 379 380 381 382 383 384 385
## 29493.13 34145.75 27115.05 35431.83 45887.40 49495.11 47748.02 49763.32
## 386 387 388 389 390 391 392 393
## 56057.22 65655.15 58785.95 53188.57 52914.05 60381.15 60910.73 69682.21
## 394 395 396 397 398 399 400 401
## 58658.67 60244.14 59799.03 59276.77 57745.72 56492.60 43094.14 51781.65
## 402 403 404 405 406 407 408 409
## 50770.21 49724.17 56201.42 48655.41 47958.38 46265.20 41891.14 40707.98
## 410 411 412 413 414 415 416 417
## 38748.87 32772.92 40739.16 43700.24 38309.31 33339.25 48387.46 52277.90
## 418 419 420 421 422 423 424 425
## 56253.82 48633.56 44913.21 43573.46 47202.52 35484.10 35210.09 29400.70
## 426 427 428 429 430 431 432 433
## 35056.93 43483.43 50457.43 47184.46 44218.34 41106.99 40993.86 37429.83
## 434 435 436 437 438 439 440 441
## 33522.34 30723.53 32318.34 34189.69 32170.60 37094.58 43373.83 40087.60
## 442 443 444 445 446 447 448 449
## 39790.10 42832.25 40693.20 44730.12 39920.54 30846.26 29669.91 41161.80
## 450 451 452 453 454 455 456 457
## 40839.55 46557.90 42142.17 42495.99 44135.75 47898.15 37652.07 42559.00
## 458 459 460 461 462 463 464 465
## 37927.85 45586.87 49148.75 51800.46 48491.13 50859.74 51063.66 52831.47
## 466 467 468 469 470 471 472 473
## 52323.10 55301.86 52598.37 57709.61 50889.41 48471.93 47041.27 43625.49
## 474 475 476 477 478 479 480 481
## 47426.27 54978.35 49339.04 51062.53 45789.41 44149.14 47015.76 36304.11
## 482 483 484 485 486 487 488 489
## 29789.11 31680.67 34409.40 35917.09 36894.44 30459.62 43138.84 49878.22
## 490 491 492 493 494 495 496 497
## 56789.72 51438.31 56328.83 64053.26 67909.98 54002.90 44468.73 42470.23
## 498 499 500 501 502 503 504 505
## 42797.27 43597.84 38019.01 40446.47 45811.00 51560.17 52278.27 52390.15
## 506 507 508 509 510 511 512 513
## 46017.21 47373.11 43587.86 46376.07 46037.79 39678.86 40823.39 39988.73
## 514 515 516 517 518 519 520 521
## 41107.50 43778.91 36533.93 31757.19 55930.82 64188.50 67884.86 61185.25
## 522 523 524 525 526 527 528 529
## 62540.65 76287.75 83347.36 57999.91 52809.00 49464.05 53895.06 53395.53
## 530 531 532 533 534 535 536 537
## 43462.47 48513.60 61324.52 55780.30 59218.31 63253.45 60271.04 55242.76
## 538 539 540 541 542 543 544 545
## 48627.37 47267.30 55298.70 55046.16 47517.82 49767.86 49592.47 50294.54
## 546 547 548 549 550 551 552 553
## 40832.48 32571.26 36964.64 45177.62 44972.18 46698.32 40637.01 49757.00
## 554 555 556 557 558 559 560 561
## 50926.03 40583.85 50238.10 58194.24 57551.64 61192.06 56909.05 68809.17
## 562 563 564 565 566 567 568 569
## 85695.89 75669.27 64097.08 68568.75 66670.88 67871.60 59341.55 43140.60
## 570 571 572 573 574 575 576 577
## 50232.42 56207.61 57404.41 59504.57 60159.29 57251.62 69643.57 58890.88
## 578 579 580 581 582 583 584 585
## 52539.40 60231.13 61752.96 54765.07 61105.95 56629.61 53639.11 67370.17
## 586 587 588 589 590 591 592 593
## 52625.90 60057.26 59138.88 52786.65 52083.56 52362.88 42968.62 45798.63
## 594 595 596 597 598 599 600 601
## 40361.36 44657.51 53524.70 46776.86 52705.33 55111.31 60847.59 56960.19
## 602 603 604 605 606 607 608 609
## 61831.00 53299.69 55201.98 55987.51 58323.04 58903.89 58439.21 52547.61
## 610 611 612 613 614 615 616 617
## 59692.96 57730.07 54792.85 51459.91 44339.43 55986.12 59919.62 50747.61
## 618 619 620 621 622 623 624 625
## 61272.88 65507.41 58920.05 81410.98 66357.47 58595.91 60622.58 55920.84
## 626 627 628 629 630 631 632 633
## 46141.71 56975.97 37301.92 37160.49 46842.14 57448.98 55470.43 84539.75
## 634 635 636 637 638 639 640 641
## 74609.72 76836.53 78493.22 73157.09 65790.13 62384.58 50146.30 48495.29
## 642 643 644 645 646 647 648 649
## 47373.75 45838.03 44203.56 46912.10 51584.16 66732.25 81327.26 78416.57
## 650 651 652 653 654 655 656 657
## 79331.03 85272.00 98875.78 93572.36 63490.59 60912.25 57830.58 58826.86
## 658 659 660 661 662 663 664 665
## 55225.71 45525.00 47937.55 52306.43 51489.07 63187.67 63063.92 63356.87
## 666 667 668 669 670 671 672 673
## 51675.70 53051.31 54081.82 49365.43 43273.95 46344.76 43915.16 47443.20
## 674 675 676 677 678 679 680 681
## 45169.04 37921.91 32516.71 32514.19 35295.71 40083.69 42370.85 40351.76
## 682 683 684 685 686 687 688 689
## 40375.63 40829.70 35105.16 41509.42 41000.93 41303.73 43322.81 54157.20
## 690 691 692 693 694 695 696 697
## 62714.53 70858.94 60029.23 55989.79 52835.26 58050.29 48228.35 41852.88
## 698 699 700 701 702 703 704 705
## 35676.42 32356.73 30818.73 36390.48 34633.54 35314.14 41317.24 70315.59
## 706 707 708 709 710 711 712 713
## 76546.99 94301.58 90577.32 93203.27 108406.03 107232.97 84327.23 84678.65
## 714 715 716 717 718 719 720 721
## 75914.62 72983.09 70935.71 53461.32 48805.29 52338.63 49813.95 38800.51
## 722 723 724 725 726 727 728 729
## 44434.26 40661.51 42933.14 40783.23 31380.77 35378.16 36010.78 29571.17
## 730 731 732 733 734 735 736 737
## 47852.19 50126.35 48137.06 53857.68 46174.67 46452.47 54528.06 40819.73
## 738 739 740 741 742 743 744 745
## 37189.41 45790.93 42527.43 44047.74 46849.09 45951.80 42328.68 49399.91
## 746 747 748 749 750 751 752 753
## 44362.29 65571.68 70954.65 67018.13 58858.43 78870.52 71863.18 70769.67
## 754 755 756 757 758 759 760
## 55832.96 105127.00 122398.61 127041.63 108349.97 110806.84 110045.42
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8312
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.912477 0.4896503 2.902163
## t2* 2468.685496 31.3623993 338.555908
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.30783 6.044789 11.75553
## 2 lag_depvar 1981.69375 2478.855621 3090.71001Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Sep 09 00:47:43 2024
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## =-=-=-=-= Iteration 8000 Mon Sep 09 00:48:12 2024
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 4.081250 | 5.195333 | 5.410333 | 5.849167 | 6.334807 |
| Comida | 332.209000 | 366.009167 | 312.386750 | 317.896583 | 341.422017 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 96.293625 | 38.104750 | 47.072333 | 29.523000 | 42.000088 |
| Enceres | 34.359750 | 18.259750 | 24.219750 | 14.801167 | 25.094561 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.751983 |
| Gas/Bencina | 62.041250 | 42.636000 | 45.575000 | 13.583667 | 34.125403 |
| Diosi | 38.008625 | 55.804250 | 31.180667 | 52.687833 | 42.554351 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 21.991250 | 12.829167 | 25.156667 | 19.086917 | 19.125754 |
| Netflix | 2.087125 | 8.713833 | 7.151583 | 7.028750 | 6.849018 |
| Otros | 102.375000 | 5.481667 | 5.000000 | 0.000000 | 16.575088 |
| Total | 693.446875 | 563.738000 | 505.988083 | 474.453167 | 542.833070 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2444, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-09-09 00:04:58 sería de: 38.557 pesos// Percentil 95% más alto proyectado: 41.724,22
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37873.02 | 37871.98 |
| Lo.80 | 37919.40 | 37932.49 |
| Point.Forecast | 38557.45 | 40369.97 |
| Hi.80 | 40345.99 | 45170.92 |
| Hi.95 | 41325.73 | 47712.38 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(3,1,0)
##
## Coefficients:
## ar1 ar2 ar3
## -0.8424 -0.7260 -0.4724
## s.e. 0.1183 0.1313 0.1236
##
## sigma^2 = 35220: log likelihood = -438.29
## AIC=884.59 AICc=885.24 BIC=893.35paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,0) errors
##
## Coefficients:
## intercept xreg
## 344.0162 21.9482
## s.e. 199.6139 6.2285
##
## sigma^2 = 32946: log likelihood = -442.54
## AIC=891.08 AICc=891.46 BIC=897.7forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 895.2852 | 963.7221 | 804.6098 |
| Lo.80 | 1018.4244 | 1093.8587 | 900.4450 |
| Point.Forecast | 1251.0399 | 1339.6925 | 1113.7263 |
| Hi.80 | 1483.6554 | 1585.5263 | 1379.8025 |
| Hi.95 | 1606.7946 | 1715.6629 | 1546.3794 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))